FIG. 1 illustrates a prior art distributed facility management system 100. FIG. 1 is based on modified versions of FIGS. 1 and 3 of U.S. Pat. No. 6,816,811 “Method of Intelligent Data Analysis to Detect Abnormal Use of Utilities in Buildings” (Seem). The prior art system comprises a central computer system 102 and a communications network 110 in communication with a plurality of building management systems 112 located in a plurality of facilities 104, 106 and 108. The system reads in utility data from one or more utility monitors 114 in a building. The system then classifies said utility data into normal 122 and anomaly 124 data. In order to determine if data is normal or an anomaly, the system computes an extreme studentized deviate based on the value of each data point and the mean and standard deviation for the set of data read in from the particular monitor in the particular monitored building. A percentile for each data point is computed for each extreme studentized deviate. If the percentile is extreme, then the data point is characterized as an anomaly. As used herein, a “percentile” is a value of a cumulative distribution that indicates what percentage of a reference population used to determine the cumulative distribution is less than a given value. Percentile may be expressed as a percentage (e.g. 0 to 100) or a fraction (e.g. 0 to 1.0). It may also be expressed as an equal division of a whole, such as a quartile, quintile or higher order division.
The Seem system has a number of significant flaws. The extreme studentized deviate is based on the assumption that the data are normally distributed, random numbers which are independent of each other. This is clearly not the case for the examples provided in Seem. The data characterized as normal 122 by Seem is not random, but periodic. Hence Seem's fundamental assumption of random and independent data is not true. This may account for the false warnings Seem experiences (Seem column 4 line 33). An additional flaw of Seem is that there is no way to compare the utility consumption of one building to another. Building 104 might be a residential facility. Building 106 might be a retail facility. Building 108 might be a manufacturing facility. One would not expect facilities in these different facility classes (e.g. industrial classes) to have comparable utility consumption patterns. Even if all facilities were in the same facility class, however, it still would not be possible to compare facilities of substantially different sizes 125. Two apartment buildings, for example, might have very different utility usage patterns if one had 2 apartments and the other had 100 apartments.
There is a need, therefore, for a system and method for comparing different facilities to determine how the facilities compare to a norm relative to their facility class and size.